Introduction: A Cost Structure Is Collapsing

For more than a decade, the central tension in software development has been that engineering capacity has struggled to keep pace with demand. Writing a line of code, fixing a defect, or migrating a legacy system all consume scarce engineer hours. As AI has evolved from a code-completion tool into an agent capable of executing tasks autonomously, this cost structure is collapsing rapidly. An increasingly persuasive view in the industry is that translating requirements into code—“programming” itself—is being solved, and what will determine success next is how organizations structure and use that capability.

This article follows that trend. It outlines the evolution from AI-assisted coding to fully autonomous agents and analyzes the cascading effects of a sharp drop in coding costs on business competition, developer capability, and organizational structure.

1. From Completing One Line to Taking Over Autonomously: The Three-Level Leap in Agent Capability

To understand this shift, it helps to clarify the stages of AI coding capability:

• Completion level: The model predicts the next few lines of code based on context. This is essentially “smarter autocomplete,” with the human remaining the sole decision-maker.
• Conversational level: Developers describe intent in natural language, and the model generates functions, tests, or even complete modules. Humans review and integrate the output.
• Agentic level: The model can read an entire codebase, plan tasks autonomously, write and run code, self-correct based on errors, and ultimately deliver working changes without step-by-step intervention.

The real leap occurs at the third stage. Agents are no longer limited to generating snippets; they can pick up unfamiliar languages or frameworks by reading source code directly. They do not have to rely entirely on examples from training data; they can “learn on the fly” within a runtime environment. This is the dividing line between a “tool” and a “digital employee.”

A balanced view is important: these capabilities still have boundaries. Implicit business rules in large legacy systems, unspoken contracts across team interfaces, and trade-offs in performance and security still require human judgment. The more accurate version of “programming has been solved” is that the marginal cost of the coding step is approaching zero, while the value of architectural decisions, requirements clarification, and quality control is being amplified.

2. Loops and Routines: Turning AI into a Sustainable Digital Employee

A single conversation is only a “point” in an agent’s lifecycle. Real value is released when the agent runs continuously in a “loop.” The industry often refers to this pattern as a “Lightning Loop.” The core idea is to attach an AI agent to scheduled tasks so that it repeatedly executes the cycle of “perceive—decide—act—verify” while unattended.

Conceptual illustration (pseudocode; structure only):

Trigger a routine every N minutes:
  1. Pull pending signals        # New defect tickets, failed CI jobs, external feedback, monitoring alerts
  2. Classify and prioritize     # Separate defects, requests, and noise
  3. Generate changes or plans   # Write patches / draft proposals
  4. Run verification in an isolated environment   # Execute tests, static checks, builds
  5. If passing, submit a PR; if failing, read logs and self-correct
  6. Record decision traces for human audit

This routine turns an agent from a “tool that is called” into an “employee that is continuously on duty.” Typical scenarios include:

• Automated CI repair: Automatically locate build or test failures and propose fix patches.
• Feedback classification: Aggregate, deduplicate, and tag large volumes of external feedback, then output structured insights.
• Dependency and compliance inspection: Regularly check dependency vulnerabilities, licenses, and configuration drift.

The key to engineering implementation is not the model itself, but the supporting infrastructure: container platforms provide isolated and reproducible runtime environments; message queues connect triggers and execution; object storage preserves code and artifacts; logging and monitoring services ensure observability and safety boundaries. For example, general cloud capabilities such as containers, messaging, storage, logging, and monitoring—such as those provided by Baidu AI Cloud—can serve as a standard foundation for running “digital employees” stably over time. In addition, humans must retain audit and circuit-breaker mechanisms: any automatically submitted change should be traceable and can be rolled back with one click.

3. When Coding Costs Approach Zero, Which Moats Erode and Which Remain Strong?

A sharp drop in coding costs will reshuffle the moats of business competition. It is useful to distinguish between what is depreciating and what remains solid:

Moats that are depreciating:

• Switching costs: When migrating code and rewriting workflows becomes cheap, user stickiness based on “it is too much trouble to switch” is significantly weakened.
• Process and engineering capability: Customized processes and integration capabilities that previously required large amounts of engineering labor can be reassembled quickly by AI and are unlikely to form barriers.

Moats that remain strong:

• Network effects: The positive feedback loop in which more users create more value is unrelated to coding costs.
• Economies of scale: Structural advantages from declining unit costs as scale increases still hold.
• Monopolistic resources: Scarce assets such as proprietary data, licenses, channels, and physical assets cannot be replaced by generated code.

For startup teams, the conclusion is clear: do not compete with large companies on coding capacity alone. Instead, use an AI-native approach to build asymmetric advantages in network effects or exclusive resources.

4. Software Development Will Become as Common as Sending Text Messages

A compelling analogy is the printing press of the 15th century. When the marginal cost of copying text dropped sharply, literacy and writing changed from privileges of a few into mass skills, triggering a revolution in the spread of knowledge. Software development is at a similar inflection point: when the cost of turning an idea into runnable software approaches zero, programming will shift from a specialized skill to general literacy.

This produces a counterintuitive result: the people best suited to write certain kinds of software are often not engineers, but those who understand the domain most deeply. When coding is no longer expensive, deep understanding of the business itself becomes the only scarce resource. For example, the best person to build a polished accounting system may well be an experienced accountant rather than a general-purpose engineer, because that accountant possesses domain knowledge and real-world constraints that engineers do not have.

5. Organizational Process Redesign Is More Decisive Than Technological Lead

An often-overlooked fact is that leaders often do not win because they possess models that others do not have. They win because they have redesigned organizational processes around AI.

When coding costs approach zero, what really creates separation is the redesign of collaboration: enabling product, finance, design, and all other functions to participate directly in “building” with AI, rather than passing requirements through multiple layers to an engineering team for scheduling. This means:

• Requesters can directly turn ideas into prototypes or even runnable code, compressing validation cycles from weeks to hours.
• Engineering teams shift from “implementers” to “architects and quality gatekeepers,” focusing on system design, review, and risk control.
• Data queries and business logic can be generated immediately by models, reducing cross-departmental information loss.

In other words, technical tools are relatively easy to obtain. Embedding tools into processes and reshaping division of labor and collaboration is the organizational capability that is difficult to replicate.

Conclusion: Preparing for the Era of Zero-Cost Coding

Overall, the core of the judgment that “programming has been solved” is that the era of coding as a scarce skill is ending. For individuals, this means shifting focus from “how fast can I write” to “how clearly can I think and how accurately can I judge.” Domain knowledge, system design capability, and problem-definition ability will become unprecedentedly important. For organizations, this means embedding AI agents into R&D processes as early as possible while using observable, auditable, and rollbackable engineering guardrails to protect quality and security.

Technology will continue to evolve rapidly, but the real dividing line is whether an organization can move first from “using AI to write code” to “using AI to redesign how work is done.”